Detection of Interictal Spikes and Artifactual Data Through Orthogonal Transformations

Abstract

This study introduces an integrated algorithm based on the Walsh transform to detect interictal spikes and artifactual data in epileptic patients using recorded EEG data. The algorithm proposes a unique mathematical use of Walsh-transformed EEG signals to identify those criteria that best define the morphologic characteristics of interictal spikes. EEG recordings were accomplished using the 10-20 system interfaced with the Electrical Source Imaging System with 256 channels (ESI-256) for enhanced preprocessing and on-line monitoring and visualization. The merits of the algorithm are: (1) its computational simplicity; (2) its integrated design that identifies and localizes interictal spikes while automatically removing or discarding the presence of different artifacts such as electromyography, electrocardiography, and eye blinks; and (3) its potential implication to other types of EEG analysis, given the mathematical basis of this algorithm, which can be patterned or generalized to other brain dysfunctions. The mathematics that were applied here assumed a dual role, that of transforming EEG signals into mutually independent bases and in ascertaining quantitative measures for those morphologic characteristics deemed important in the identification process of interictal spikes. Clinical experiments involved 31 patients with focal epilepsy. EEG data collected from 10 of these patients were used initially in a training phase to ascertain the reliability of the observable and formulated features that were used in the spike detection process. Three EEG experts annotated spikes independently. On evaluation of the algorithm using the 21 remaining patients in the testing phase revealed a precision (positive predictive value) of 92% and a sensitivity of 82%. Based on the 20- to 30-minute epochs of continuous EEG recording per subject, the false detection rate is estimated at 1.8 per hour of continuous EEG. These are positive results that support further development of this algorithm for prolonged EEG recordings on ambulatory subjects and to serve as a support mechanism to the decisions made by EEG experts.

A Comparative Study of Intracranial EEG Files Using Nonlinear Classification Methods

“…14,18,24,26,30 Researchers have thus considered different approaches using a diversity of linear and nonlinear parameters in order to automate processes of seizure detection, eliciting a better understanding of the chaotic dynamics in biological systems, 6,15,21,23 and where promising results have been substantiated. 1,2,16,29,31 In using subdural EEG data in this study, we have taken into consideration the fact that there is a considerable attenuation effect of the skull on the scalp EEG. This data generally reveal very little fast activity exceeding the beta range [>30 Hz], limiting as a result the application scope of seizure detection algorithms that rely on scalp EEG recordings since there is a limitation in the use of higher frequency components.…”

Automated Epileptic Seizure Detection Methods: A Review Study

“…Their results showed that the most discriminative features for neonatal seizure detection 1 are morphological based features, such as amplitude, shape and duration of waveforms. In addition, time domain features such as statistical features (Adjouadi et al, 2005), Hjorth's descriptors (Hjorth, 1970), nonlinear features (Kannathal, Acharya, Lim, & Sadasivan, 2005;McSharry, et al, 2002)-correlation dimension (Elger & Lehnertz, 1998), Lyapunov exponent Ubeyli, 2006;Ubeyli, 2010b) and other features obtained from convolution kernels (Adjouadi et al, 2004), eigenvector methods (Naghsh-Nilchi & Aghashahi, 2010 ; Ubeyli, 2008aUbeyli, , 2008bUbeyli, , 2009a, principal component analysis (PCA) (Ghosh-Dastidar, Adeli, & Dadmehr, 2008;Hesse & James, 2007;James & Hesse, 2005;Polat & Gunes, 2008a;Subasi & Gursoy, 2010), ICA (Hesse & James, 2007;James & Hesse, 2005;Subasi & Gursoy, 2010), crosscorrelation function (Chandaka, Chatterjee, & Munshi, 2009;Iscan, et al, 2011), and entropy (Guo, Rivero, Dorado, et al, 2010;Kannathal, Choo, Acharya, & Sadasivan, 2005;Liang, Wang, & Chang, 2010;Naghsh-Nilchi & Aghashahi, 2010 ;H. Ocak, 2009;Srinivasan, Eswaran, & Sriraam, 2007;Wang, et al, 2011) have been proposed to characterize the EEG signal.…”

A Novel Signal Processing Method for Intraoperative Neurophysiological Monitoring in Spinal Surgeries

“…A difference operator can be used but they have a serious drawback of being highly susceptible to noise, and thus a smoothing operator need to be included to improve the differentiator's signal to noise ratio (SNR). The Walsh differentiation method (Adjouadi et al, 2004(Adjouadi et al, , 2005Weide et al, 1978) was utilized to overcome the problem, as described in the following section.…”

“…Detection of the peak that coincides with the occurrence of the SSEP was automated using the Walsh transformation method (Weide et al, 1978;Smith, 1981;Adjouadi et al, 2004Adjouadi et al, , 2005 to indicate the evoked potential response.…”

“…The broad scope of applicability of the Walsh transform yielded, as examples, excellent results in (a) extracting stereo features to recover depth information in 2-D images (Adjouadi and Candocia, 1994;Adjouadi et al, 1996;Candocia and Adjouadi, 1997) and (b) in detecting interictal spikes in EEG data as means to detect seizures in pediatric epilepsy (Adjouadi et al, 2004(Adjouadi et al, , 2005Tito et al, 2010). In this SSEP application, a second order Walsh operator was found to be extremely effective in localizing the SSEP under only 10 trials even when the morphology of this signal is not yet quite similar as that of the so-called true morphology obtained with a much larger number of trials (200 to 500).…”